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Convenient and efficient development of Machine Learning Interatomic Potentials
Angular relational knowledge distillation of ML interatomic potentials I LeMaterial Reading Group
AMS2020 new features: QM/MM, machine learning potentials, efficient G0W0 method
Beyond Interatomic Potentials - Further Acceleration of Atomic-Scale SImulations
Automating the composition of ML interatomic potentials in Julia | Emmanuel Lujan | JuliaCon 2023
Matlantis Webinar with MIT Professor Ju Li: Universal Machine Learning Interatomic Potential
Dr. Volker Deringer (Oxford) --- Machine-learned interatomic potentials for materials chemistry
Daniel Schwalbe Koda: Machine learning for interatomic potentials
Jigyasa Nigam - Incorporating physical constraints and symmetry in atomic-scale machine learning
Let's Talk Research Episode 3: Machine-learned interatomic potentials (MLIPs)
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Last Updated: August 19, 2026
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